DOC: update bottleneck repo and documentation urls

doc/source/getting_started/install.rst

@@ -218,7 +218,7 @@ Recommended dependencies
   ``numexpr`` uses multiple cores as well as smart chunking and caching to achieve large speedups.
   If installed, must be Version 2.6.2 or higher.
 
-* `bottleneck <https://github.com/kwgoodman/bottleneck>`__: for accelerating certain types of ``nan``
+* `bottleneck <https://github.com/pydata/bottleneck>`__: for accelerating certain types of ``nan``
   evaluations. ``bottleneck`` uses specialized cython routines to achieve large speedups. If installed,
   must be Version 1.2.1 or higher.
 

doc/source/whatsnew/v0.21.0.rst

@@ -20,7 +20,7 @@ Highlights include:
 - Integration with `Apache Parquet <https://parquet.apache.org/>`__, including a new top-level :func:`read_parquet` function and :meth:`DataFrame.to_parquet` method, see :ref:`here <whatsnew_0210.enhancements.parquet>`.
 - New user-facing :class:`pandas.api.types.CategoricalDtype` for specifying
   categoricals independent of the data, see :ref:`here <whatsnew_0210.enhancements.categorical_dtype>`.
-- The behavior of ``sum`` and ``prod`` on all-NaN Series/DataFrames is now consistent and no longer depends on whether `bottleneck <http://berkeleyanalytics.com/bottleneck>`__ is installed, and ``sum`` and ``prod`` on empty Series now return NaN instead of 0, see :ref:`here <whatsnew_0210.api_breaking.bottleneck>`.
+- The behavior of ``sum`` and ``prod`` on all-NaN Series/DataFrames is now consistent and no longer depends on whether `bottleneck <https://bottleneck.readthedocs.io>`__ is installed, and ``sum`` and ``prod`` on empty Series now return NaN instead of 0, see :ref:`here <whatsnew_0210.api_breaking.bottleneck>`.
 - Compatibility fixes for pypy, see :ref:`here <whatsnew_0210.pypy>`.
 - Additions to the ``drop``, ``reindex`` and ``rename`` API to make them more consistent, see :ref:`here <whatsnew_0210.enhancements.drop_api>`.
 - Addition of the new methods ``DataFrame.infer_objects`` (see :ref:`here <whatsnew_0210.enhancements.infer_objects>`) and ``GroupBy.pipe`` (see :ref:`here <whatsnew_0210.enhancements.GroupBy_pipe>`).
@@ -390,7 +390,7 @@ Sum/Prod of all-NaN or empty Series/DataFrames is now consistently NaN
 
 
 The behavior of ``sum`` and ``prod`` on all-NaN Series/DataFrames no longer depends on
-whether `bottleneck <http://berkeleyanalytics.com/bottleneck>`__ is installed, and return value of ``sum`` and ``prod`` on an empty Series has changed (:issue:`9422`, :issue:`15507`).
+whether `bottleneck <https://bottleneck.readthedocs.io>`__ is installed, and return value of ``sum`` and ``prod`` on an empty Series has changed (:issue:`9422`, :issue:`15507`).
 
 Calling ``sum`` or ``prod`` on an empty or all-``NaN`` ``Series``, or columns of a ``DataFrame``, will result in ``NaN``. See the :ref:`docs <missing_data.numeric_sum>`.
 

doc/source/whatsnew/v0.8.1.rst

@@ -29,7 +29,7 @@ Performance improvements
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
   - Improved implementation of rolling min and max (thanks to `Bottleneck
-    <http://berkeleyanalytics.com/bottleneck/>`__ !)
+    <https://bottleneck.readthedocs.io>`__ !)
   - Add accelerated ``'median'`` GroupBy option (:issue:`1358`)
   - Significantly improve the performance of parsing ISO8601-format date
     strings with ``DatetimeIndex`` or ``to_datetime`` (:issue:`1571`)

pandas/_libs/window.pyx

@@ -1162,7 +1162,7 @@ def roll_median_c(ndarray[float64_t] values, int64_t win, int64_t minp,
 
 # Moving maximum / minimum code taken from Bottleneck under the terms
 # of its Simplified BSD license
-# https://github.com/kwgoodman/bottleneck
+# https://github.com/pydata/bottleneck
 
 
 cdef inline numeric init_mm(numeric ai, Py_ssize_t *nobs, bint is_max) nogil: